73 results about "Electric power demand" patented technology

The invention provides a micro-grid system supply and demand balance and green energy scheduling optimization method and system, which can accurately match the user side power demand and green energy production, and can call an energy storage system to release stored electric energy or buy electricity through a main power grid to complement power vacancy when there is a supply and demand electric quantity difference in a power generation trough and a power consumption peak period; in the peak period of solar power generation, photovoltaic electric energy is preferentially used for meeting the load requirement, when the total power generation amount is larger than the total power consumption amount, redundant power can be used for charging an energy storage device, efficient consumption of renewable energy sources is achieved, meanwhile, the stable and continuous characteristics of wind power generation are utilized, basic power is provided for loads in the whole time period day and night, and energy is saved. And in the solar valley stage and the load valley stage, the energy storage system is matched to carry out charging and discharging scheduling and stabilize power fluctuation, challenges caused by load peak-valley difference and renewable energy intermittency can be effectively handled through multi-energy cooperative control logic, and the reliability, economical efficiency and clean energy permeability of micro-grid operation are improved.

Changing electrical power needs for buildings often requires the upgrade of the electrical panel, the addition of extra panels, or both. Herein, a single electrical panel hosts power interface modules that connect to multiple power rails within the panel. The modules, which plug into the panel, convert power between the rails, or make and break connections between the rails, under the command of a system controller. The rails are connected to switchgear to which a variety of external power sources and loads can be connected. The modules are cooled by a heat exchanger and the waste heat may be directed outside or used to heat inside the building or heat domestic water.

The invention belongs to the technical field of electric power, and discloses an industrial chain electricity consumption relation analysis method, system and device based on a convergence cross-mapping algorithm and a medium, and the method comprises the following steps: obtaining electricity consumption time sequence data of each industry; decomposing the time sequence data through a wavelet decomposition method to obtain multi-band power consumption data; performing multi-band causal analysis on the multi-band power consumption data through a convergence cross mapping method, and constructing a global causal relationship network matrix; constructing a power consumption conduction directed graph according to the global causal relationship network matrix to represent the power demand conduction relationship of each industry; and taking the electricity consumption conduction directed graph as input, and calculating actual electricity consumption according to the electricity demand conduction relation of each industry to obtain an industry chain electricity consumption evaluation result.

A grid management platform is disclosed for predicting and mitigating peak electricity demand events using probabilistic modeling and cost-optimized control. The system includes a likelihood model that evaluates the probability of each remaining day within an evaluation interval being the peak load day. This is achieved through a Monte Carlo simulation engine that generates multiple stochastic realizations of future load trajectories based on historical data, forecast data, and forecast error profiles derived from back testing. The system computes likelihood scores and classifies days using optimized bin thresholds that minimize operational cost, considering demand charges, available distributed energy resources, and forecast uncertainty. Based on the predicted likelihoods, the platform generates actionable insights and transmits control signals to grid-connected assets such as batteries, HVAC systems, and electric vehicle chargers to shift or reduce load during predicted peak periods.

An object of the present invention is to appropriately evaluate a temporally and spatially distributed and fluctuating electric power demand pattern, and accurately estimate a location and scale at which an electric power supply base needs to be installed. An information processing apparatus according to the present invention transforms electric power demand information into a demand function on a spatial axis, generates an integral kernel for evaluating a required supply amount, and estimates an installation location or required scale of an electric power supply point from a result of performing convolution on the demand function and the integral kernel.

An electrical grid system includes: a first obtaining controller that obtains the electric power demand or supply request information from the electric power transmission and distribution facility; a second obtaining controller that obtains an electric storage state information; a third obtaining controller that obtains a vehicle information of an electric vehicle; a supply and demand satisfaction level calculation controller that calculates a supply and demand satisfaction level based on the electric power demand or supply request information; a selection controller that selects an electric vehicle to be guided to the charge or discharge spot based on the vehicle information obtained by the third obtaining controller and the supply and demand satisfaction level calculated by the supply and demand satisfaction level calculation controller; and a communication controller that sends a guide information to the selected electric vehicle.

A power system for a tethered electric work machine. The power system includes an electric work machine input that receives power from a tether of the tethered electric work machine, a battery module to supply power to the power system, and a controller. The controller obtains an instantaneous electrical power demand for a motor of the tethered electric work machine and determines a moving average power demand for the motor based on a power supplied to the motor over a preceding predetermined time period. input Based on the moving average power demand, the controller controls the power system to supply power from the input to the battery module or supply power from the battery module to the motor based on a difference between the moving average power demand and the instantaneous electrical power demand for the motor.

This application discloses an appliance power decomposition method and storage medium. The method includes: collecting the raw current signal, total active power data, actual power data of the target appliance, and electricity consumption scenario information from the user's main electricity meter; extracting frequency domain feature vectors from the raw current signal, performing one-hot encoding on the electricity consumption scenario information to obtain scenario feature vectors, and normalizing the total active power data and actual power data; constructing a neural network model including a two-layer sliding window; inputting the processed data into the neural network model for training and updating the model parameters; inputting the bus observable signal at any time into the trained neural network model, and outputting the power decomposition result of the target appliance. The advantage of this application is that it improves the accuracy of appliance power decomposition, providing accurate data support for grid energy management and electricity demand response.

The embodiment of the invention provides a charging method and device for a battery in a vehicle, the vehicle and a computer readable storage medium, and the method comprises the steps: determining the working state of a photovoltaic cell panel in the vehicle in response to the vehicle being in a flameout state; in response to the working state, indicating that the photovoltaic cell panel is in a working mode, controlling the photovoltaic cell panel to charge a first battery in the vehicle, the first battery being used for supplying power to electrical equipment in the vehicle when the vehicle is in a flameout state; in the process that the photovoltaic cell panel charges the first cell, the charge state of the first cell and the charge state of a second cell in the vehicle are detected, and the second cell is used for supplying power to a function controller in the vehicle; determining a battery charging strategy of the vehicle based on the charge state of the first battery and the charge state of the second battery; and charging the second battery according to the battery charging strategy. The technical problem that in the related technology, when the state of charge of the power battery is low, the power requirement of the vehicle cannot be met is solved.

The present application relates to the technical field of power load prediction, in particular to a power demand medium-term prediction method based on multi-dimensional component decomposition, comprising: first, obtaining historical power load data of a target area and a multi-dimensional associated data set containing economic indicators, meteorological time series and date type information; performing complete ensemble empirical mode decomposition based on adaptive noise on the historical load data to obtain intrinsic mode components of different time scales; then performing feature extraction and synchronicity alignment on the associated data set to generate an associated feature set of each component time alignment; using an improved spatiotemporal graph convolution network which constructs a dynamic graph structure according to the component dynamics coupling relationship and dynamically updates the node connection weight according to the prediction error, jointly predicting the future sequence of each component and the corresponding associated feature to generate the power demand medium-term prediction result after aggregation. This method improves the fitting degree and accuracy of medium-term power demand prediction through fine decomposition and dynamic spatiotemporal modeling.

A system and method are provided for simulating the sizing and siting of energy generation facilities to supply electricity for a recommended industrial load. The method begins by accessing specific criteria for each land parcel in a portfolio and filtering out unviable parcels based on factors such as existing wind or solar installations, protected areas, or parcel size. An objective function is calculated to represent the overall cost of generating electricity to meet the load. The system executes multiple design simulations, generating random placements of energy facilities based on criteria like industrial load size and location, renewable energy resource availability, and transmission line proximity. For each simulation, the cost of construction and electricity delivery is calculated to identify the lowest possible cost. Using a probabilistic technique, the method approximates the global minimum cost and ranks each simulation by cost-efficiency, helping identify optimal land parcels for energy facility placement.

The invention discloses a green electric power energy storage and consumption system integrating functions of vehicle battery replacement, mobile energy supply and vehicle-power grid interaction (V2G). The system is composed of key components such as a photovoltaic array, a power conversion system, a power grid, a battery charging and replacing station, batteries in low-electric-quantity and full-electric-quantity states, a power and energy storage integrated battery bin, an electric vehicle and an electricity load. The power and energy storage integrated battery bin serves as a system core assembly and can adapt to various application scenes such as energy transmission (V2V) between vehicles, energy interaction (V2G) between the vehicles and a power grid and energy supply (V2L) between the vehicles and loads at the same time. In an energy network integrated by a photovoltaic system, the system has the capability of directly providing green power supply for a charging and battery swapping station, and meanwhile, redundant electric energy can be transmitted to a power grid to realize power grid connection. In an emergency, the power grid can provide necessary power support for the charging and battery swapping station; the charging and battery replacing station is mainly responsible for replacing the low-power battery and providing the fully-charged battery for the vehicle and the power storage integrated battery compartment so as to meet the power requirement of the vehicle and the power storage integrated battery compartment. The invention provides an innovative solution for key scenes in the technical field of electric vehicles. Specifically, the invention relates to the following aspects: aiming at the technical problem of external discharge caused by lack of a standardized battery compartment design, the technology of mutual charging between vehicles, the technology of interaction between an electric vehicle and a power grid, the technology of supplying power to electric load equipment by the electric vehicle, and the technology of emergency power failure under the condition of emergency power failure. The invention discloses a technology for supplying power to residential buildings or commercial buildings by electric vehicles.

The present disclosure provides a method for predicting coincident peak events in electricity demand. The method includes receiving historical electrical usage data and forecasted demand data for a given timeframe, training a simulation engine based on the historical data, generating a plurality of load scenarios for a target period of time using the trained simulation engine, and computing a probability that the target period of time will include a new record high peak in electricity demand based on the generated scenarios. The method enables accurate prediction of peak demand events without relying on weather or economic data, allowing utilities and consumers to better manage electricity usage and reduce costs associated with peak demand periods.

An electric power and electric quantity balance optimization method for an electricity-gas coupling system under extreme disasters comprises the following steps: firstly, establishing dynamic association between a natural gas system and an electric power system through an electricity-to-gas device and a gas power plant, and building an electricity-gas coupling model; and secondly, aiming at climate resource fluctuation caused by disasters, combining a new energy output model and source-load characteristics during the disasters, utilizing natural gas energy storage to make up an electric quantity gap of new energy output, and ensuring source-load supply and demand balance of the power system. And finally, in combination with the predicted climate data and the load side power demand, establishing a pre-disaster-disaster-time (intra-day) multi-time scale scheduling strategy by taking the maximum generating capacity of the system as a target. According to the method, the advantages of electricity-gas coupling are utilized, resources are effectively scheduled before and after an extreme disaster event occurs, through a multi-time-scale scheduling strategy, the influence of the extreme disaster on the power system source-load supply and demand relationship is reduced, and the power and electric quantity balance of the system is ensured.

A system for generating electricity by pyrolyzing organic materials and feeding the pyrolysis fluid to a battery of fuel-cells. The system includes a pyrolysis reactor receiving organic materials and producing pyrolysis fluid. The fluid pyrolysis is then separated into a plurality of sub-mixtures, each provided via a respective separator output. A plurality of fuel-cell devices for generating electricity using different technologies are each coupled to a respective separator output. A controller controls the pyrolysis reactor, the separator device, and the plurality of fuel-cell devices according to a signal representing a demand for electric power, a signal representing cost of operating at least one of the pyrolysis reactor and the fuel-cell generator, and a signal representing minimum price of electric power.

The application discloses a hybrid off-road vehicle energy management method, device, medium and product, and relates to the field of vehicle energy management. The application classifies off-road working conditions based on test data by using a K-means clustering algorithm and relative entropy to obtain a classification result, simplifies variable off-road environments into a transfer probability matrix of electric power demand under different working conditions, greatly reduces the complexity of off-road working condition classification, and realizes an energy management strategy which is adaptively changed in real time according to a driving working condition. Moreover, the application determines an optimal solution corresponding to each type of working condition by using an improved expected SARSA algorithm based on the classification result, reduces online calculation time, and further solves the problems that an energy management strategy based on traditional reinforcement learning is slow in training speed and difficult to realize real-time control of a vehicle.

The present invention provides a control system and control method that can appropriately control power demand using multiple resources controlled by different control devices. [Solution] The control system controls the amount of electricity demand, which indicates the amount of electricity supplied to the supply target from an external power source. The supply target includes a first resource and a second resource, which are configured to adjust the amount of electricity demand. The control system comprises a first control device that controls the first resource and a second control device that controls the second resource. The first control device controls the first resource independently of the second control device. When a first start condition is met, one of the first resource and the second resource starts a first adjustment operation (S15) to suppress the increase in electricity demand. When a second start condition is also met in addition to the first start condition, the other of the first resource and the second resource starts a second adjustment operation (S27) to suppress the increase in electricity demand.

A method for monitoring and controlling a vehicle battery pack that includes a plurality of battery cells. The battery cells each include a switching unit which serves to connect and disconnect the respective battery cells. Pack information of the battery pack is acquired, a pack voltage of the battery pack is calculated on the basis of the cell voltages and a current activation pattern for actuating the switching units. An electrical power requirement of the vehicle is predicted based on GPS information. A state of the battery pack is estimated based on the pack information and the current activation pattern and an optimized activation pattern for actuating the switching units on the basis of the predicted electrical power requirement and the estimated state of the battery pack is calculated. The switching units of the respective battery cells are actuated according to the optimized activation pattern.

A water and power generating system is disclosed, comprising at least one renewable energy generator, at least one submerged or floating desalination apparatus configured to be at least partially powered by the renewable energy generator, a controller, a desalinated water output conduit, and an electrical power output. The controller is configured to dynamically adjust the operation of the desalination apparatus in response to variations in available renewable energy and fluctuating demands for desalinated water and electrical power. Also disclosed is a water desalination system comprising a docking platform with a substantially tubular housing, mooring attachments, and ballast for vertical orientation. The docking platform may be connected to multiple floating desalination buoys. The platform may further include electrical and water distribution hubs for interconnection with the buoys and shore-based infrastructure. The desalination buoys may incorporate intake bubble curtain devices and brine dispersal systems to enhance intake quality and manage brine discharge.

A control method according to the present disclosure includes executing a first planning method for planning an output of a fuel cell device so as to compensate for a difference between a predicted value of a power demand side's power demand and a predicted value of an output of a solar power generation device. This control device is provided with: a memory that stores a predicted value for the power demand of a power demand side and a predicted value for the output of a solar power generation device; and a planner that executes a planning method that plans an output of a fuel cell device to compensate for a difference between a predicted value of a demand power of the power demander and a predicted value of an output of the solar power generation device.

The embodiment of the invention discloses power electronic equipment, relates to the technical field of equipment heat dissipation, and aims to improve the operation reliability of the power electronic equipment. The power electronic equipment comprises a power unit and a heat dissipation system. The power unit comprises a first power module and a second power module, and the second power module is connected with the first power module through an isolation coupling module; the withstand voltage level of the second power module is smaller than that of the first power module; the heat dissipation system comprises a heat dissipation unit. The heat dissipation unit comprises a first heat exchanger and a second heat exchanger. The first heat exchanger is at least thermally coupled with part of the first power module. A first flow channel is arranged in the first heat exchanger, and the first end of the first flow channel is used for introducing insulating heat dissipation fluid; a second heat exchanger thermally coupled with at least a portion of the second power module; a second flow channel is arranged in the second heat exchanger, and the first end of the second flow channel is used for leading in non-insulating heat dissipation fluid. The application is suitable for providing power demand.

The invention relates to the technical field of power utilization of factories, and discloses a clean intelligent energy supply system for factories, which comprises a photovoltaic power generation system for converting solar energy into electric energy which is respectively used for an energy storage system and a hydrogen energy power generation system; the hydrogen energy power generation system converts electrolytic hydrogen into electric energy and transmits the electric energy to the energy storage system; the energy storage system is responsible for storing electric energy; the electric energy distribution system distributes electric energy of the energy storage system to factory equipment. The intelligent energy supply system collects photovoltaic power, hydrogen yield, energy storage charge state and equipment power demand in real time, predicts future factory total load and key equipment fluctuation based on deep learning, generates a dynamic electric energy distribution strategy and outputs the strategy to the electric energy distribution system, and adjusts power supply power in combination with the energy storage state. Meanwhile, the voltage of key equipment is monitored, and when fluctuation exceeds a threshold value, early warning is triggered and a multi-stage response mechanism is started. According to the invention, intelligent overall planning of the electric energy distribution system is realized, and the stability and safety of factory power supply are guaranteed.

The invention belongs to the field of power utilization for aluminum oxide production, and provides a green electricity low-carbon aluminum-based polymer power utilization and production process collaborative optimization method and system. The method comprises the following steps: based on historical power generation data of a wind power plant and a photovoltaic power station, determining a daily aluminum oxide target yield and predicting a power demand of an aluminum oxide production process in one day; and determining an optimal aluminum oxide production process adjustment scheme by taking the daily minimum power cost and the maximum aluminum oxide yield as targets in combination with a multi-target demand response model based on the daily aluminum oxide target yield and the predicted power demand of the aluminum oxide production process in one day.

A system and method for efficiency and revitalization of power plant assets allows for existing fossil fuel burning power plants to operate in tandem with renewable power sources and to operate at a consistent base load. The system and method reduce fossil fuel burning power plant power / load cycling and improve efficiency, increase power plant useful lifetime, and reduce emissions. The system and method allow for substantially consistent power output regardless of power demand and power supply generation from existing fossil fuel burning power plants and renewable energy sources supplying a common grid.

The invention discloses a computing power and electric power cooperation scale prediction method, system and device and a medium, and the method comprises the steps: obtaining multi-source data, carrying out the preprocessing of the multi-source data, carrying out the data fusion and scene decoupling of the preprocessed multi-source data, obtaining a scene classification label, generating a computing power scene decoupling modeling structure through the scene classification label, and carrying out the prediction of the computing power and electric power cooperation scale. Multi-dimensional parameter correlation analysis is carried out, prediction of different computing power scales is carried out, prediction results of all the computing power scales are integrated, and a multi-dimensional computing power scale prediction model is generated; performing weight distribution on the regional total computing power scale to obtain a preliminary distribution scheme, verifying the preliminary distribution scheme to obtain a computing power scale spatial-temporal distribution prediction map, and constructing a dynamic energy consumption model library to perform power demand calculation to obtain a power demand spatial-temporal distribution prediction result; and performing comprehensive evaluation and decision packaging on the multi-dimensional computing power scale prediction model and the power demand spatial-temporal distribution prediction result to generate a standardized evaluation file, thereby realizing prediction of the computing power and power cooperation scale.

A hybrid apparatus for charging electric vehicles and mining cryptocurrency is provided. The apparatus may comprise an electric power input associated with a rated maximum power capacity, an electric vehicle charger, a cryptocurrency miner and a power coordinator. The electric vehicle charger may have at least one vehicle power outlet and a maximum charger power draw from the electric power input. The cryptocurrency miner may have multiple cryptocurrency mining computers and a maximum miner power draw from the electric power input. Each mining computer may have an adjustable intensity level. The power coordinator may be configured to determine mining power availability within the rated maximum power capacity after prioritizing power demand by the electric vehicle charger, and to direct the cryptocurrency miner to continue mining cryptocurrency by adjusting the intensity levels of the mining computers such that the cryptocurrency miner consumes not more than the mining power availability.

A system (1) including a mini-grid (101) for providing stand-alone power is provided. The mini-grid (101) comprises a plurality of appliances (10); a renewable source (20) for generating electricity from renewable energy; a generator (30) for generating electricity from combustion; a battery (40); and a controller (50) connected to the renewable source (20), the generator (30), the battery (40), and the plurality of appliances (10). The controller (50) is configured to control how power is supplied to the plurality of appliances (10) from each of the renewable source (20), the generator (30), and the battery (40), using a control algorithm. The control algorithm involves obtaining a renewable energy forecast; predicting an electrical demand resulting from the use of the plurality of appliances (10); and predicting an electrical supply from the renewable source (20) based on the renewable energy forecast. The control algorithm uses the prediction of electrical demand and the prediction of electrical supply to determine a desired state of charge of the battery (40) for minimising the use of the generator (30).

An electric power adjusting device is configured to execute a process of acquiring vehicle information of a plurality of electric vehicles connected to a power utility grid, a process of classifying, based on the vehicle information, the electric vehicles into a plurality of groups according to predetermined characteristics, and a process of adjusting a charging and discharging burden on the electric vehicles fir each of the groups when adjusting an electric power demand in the power utility grid.

A power management system is electrically coupled to a solar photovoltaic array, a power transmission network and a battery. The power management system includes a power converter and a processing module. The power converter is configured to operate in one of a plurality of operating modes, including a self-generating and self-using mode, a time electricity price mode, and a standby mode, to regulate power flowing between the solar photovoltaic array, the power transmission grid, and the battery. The processing module is configured to output prediction data representing the power demand in the first time period, the output power of the solar photovoltaic array and the electricity price of the power transmission network through a prediction model. One of a plurality of operation modes is set as a target mode of the power converter and a corresponding switching time according to a time sequence of the prediction data. And controlling the power converter to operate in the target mode at the switching time.

An automated control method for meeting rapidly fluctuating power demands with stable power production is disclosed. The method includes determining a market value of a unit of electricity sold on the grid, a fuel cost required to produce the unit of electricity, and a market value of a processing task requiring the unity of electricity. The method also includes calculating which of the electricity, processing, or fuel, is the most valuable; shutting off a running process when the value of the electricity is highest or the value of the fuel is highest; and starting a pending process when the net market value of the processing task is highest. The method may also include reducing electricity generation at a power plant when the value of electricity is negative, or exercising a futures contract to supply electricity.